TCPL: Target Consensus-Guided Prototype Learning for Open-Scene Target Detection

Spectral small target detection in open scenes is challenged by variations in target spectra and backgrounds, which limit the transferability of fixed target priors and target knowledge derived from labeled scenes and varying references. To address this challenge, we propose Target Consensus-Guided Prototype Learning (TCPL), a framework that learns transferable target knowledge for detection without adaptation to new scenes. During densely supervised training, TCPL uses sparse target points and their neighborhoods to construct local spectral–spatial references. Its asymmetric text guidance combines target and background descriptions on the support side while retaining only target semantics on the query side. By matching features from support-point neighborhoods with query-pixel features, TCPL aggregates predictions for the same query image under different support conditions into a consensus, mitigating the effects of variations among support samples. Detection supervision and knowledge distillation transfer this consensus knowledge into a learnable prototype bank and target anchor, enabling query-only inference. Furthermore, we construct three datasets with pixel-level target annotations and establish a unified open-scene evaluation setting.